Color Image Research Articles

Expression of Concern: Oil painting color image enhancement recognition method based on artificial intelligence: applications of an AI model in environmental research, E. Yao, Marvin White

Expression of Concern: Oil painting color image enhancement recognition method based on artificial intelligence: applications of an AI model in environmental research, E. Yao, Marvin White

Red dichromatic imaging and linked color imaging as reliable image-enhanced endoscopic procedures for detecting the distal end of the palisade vessels in the columnar metaplastic mucosa of the gastroesophageal junction zone.

There is a consensus that identifying the distal end of the palisade vessels (DEPV) is important for diagnosing gastroesophageal junction (GEJ). However, optimum observation methods have not been established. This study investigated the use of effective image-enhanced endoscopy (IEE) for DEPV detection. One hundred endoscopic images in 20 cases of columnar metaplastic mucosa of the GEJ recorded with white-light imaging (Olympus-WLI and Fujifilm-WLI) and IEEs (narrow-band imaging; RDI1/2/3, red dichromatic imaging; texture and color enhancement imaging 1/2; blue-laser imaging; and LCI, linked color imaging) from two manufacturers were extracted and evaluated by 10 evaluators. Up to 24 radial straight lines from the center of the lumen were placed on the image, and the evaluators placed markings according to confidence level (high, low, and not detectable) at the DEPV locations. The detectability and reproducibility at the rate of the confidence level and coefficient of variance of markings among the evaluator were analyzed. In total, 15,180 markings were obtained. In terms of detectability, RDI1 (49.4%), RDI2 (53.0%), RDI3 (54.1%), TXI2 (49.7%), and LCI (34.6%) had a significantly higher rate of high confidence among the IEEs in each manufacturer. By contrast, Olympus-WLI (40.6%), Fujifilm-WLI (17.6%), narrow-band imaging (15.9%), and blue laser imaging (9.8%) presented with a significantly lower rates of high confidence. Regarding reproducibility, RDI3 and LCI had the lowest coefficient of variance for each manufacturer. RDI and LCI could be reliable modalities for detecting DEPVs in the columnar metaplastic mucosa of the GEJ zone.

Securing Images using Bifid Cipher associated with Arnold Map

In this paper, a scheme for image data security is designed by using Bifid cipher and Arnold map. The conventional 2D-Bifid cipher is changed to handle the encryption and decryption of color images. Further, a block-based method is realized using Arnold map to encrypt and decrypt the image data of square as well as non-square sizes. The proposed scheme is constructed on a widely adopted cryptographic framework, i.e., substitution-permutation design, where Bifid cipher-based scheme acts as a substitution layer and Arnold map-based scheme acts as a diffusion layer. The designed scheme has a huge key space with key sensitivity not only to accurate keys but also to their accurate orders. The practicability and performance of the designed scheme is validated by conducting a detailed analysis along with a comparative study. This paper contributes to a simple, efficient and secure image encryption method, which outperforms to the related works.

Optimization of Execution Microscopic Extrusion Parameter Characterizations for Color Polycarbonate Grading: General Trend and Box–Behnken Designs

This research article concentrates on process conditions in addition to improving color selections in polymer compounders and developing more accurate simulation models. The feed rate (FR), temperature (T) and screw speed (SS) are three processing variables that the research investigates using general trends (GTs) and Box–Behnken design (BBD) response surface methodology. The identical set of processing settings was tweaked at three separate phases independently of one another. This study uses the experimental design to investigate process parameters’ optimization while holding all other parameters constant. This design was given the name GT. To develop this design and its statistical optimization, this study used the software of the design expert method. A regression model was run in this design, which displayed collective as well as individual effects of the parameters on color images. The values of tri-stimulus color with the best optimization had the smallest proper color variance (dE*). To obtain information on pigment characteristics, an SEM image analysis was conducted, which aids in improving future designs and overcoming manufacturing issues that affect color fluctuation properties and waste reduction for various chemical grades, both of which enhance environmentally friendly processes.

Multi-Branch Network for Color Image Denoising Using Dilated Convolution and Attention Mechanisms.

Image denoising is regarded as an ill-posed problem in computer vision tasks that removes additive noise from imaging sensors. Recently, several convolution neural network-based image-denoising methods have achieved remarkable advances. However, it is difficult for a simple denoising network to recover aesthetically pleasing images owing to the complexity of image content. Therefore, this study proposes a multi-branch network to improve the performance of the denoising method. First, the proposed network is designed based on a conventional autoencoder to learn multi-level contextual features from input images. Subsequently, we integrate two modules into the network, including the Pyramid Context Module (PCM) and the Residual Bottleneck Attention Module (RBAM), to extract salient information for the training process. More specifically, PCM is applied at the beginning of the network to enlarge the receptive field and successfully address the loss of global information using dilated convolution. Meanwhile, RBAM is inserted into the middle of the encoder and decoder to eliminate degraded features and reduce undesired artifacts. Finally, extensive experimental results prove the superiority of the proposed method over state-of-the-art deep-learning methods in terms of objective and subjective performances.

Universal scale laws for colors and patterns in imagery

Distribution of colors and patterns in images is observed through cascades that adjust spatial resolution and dynamics. Cascades of colors reveal the emergent universal property that Fully Colored Images (FCIs) of natural scenes adhere to the debated continuous linear log-scale law (slope −2.00±0.01) (L1). Cascades of discrete 2×2 patterns are derived from pixel square reductions onto the seven unlabeled rotation-free textures (0000, 0001, 0011, 0012, 0101, 0102, 0123). They exhibit an unparalleled universal entropy maximum of 1.74±0.013 at some dynamics regardless of spatial scale (L2). Patterns also adhere to the Integral Fluctuation Theorem (1.00±0.01) (L3), pivotal in studies of chaotic systems. Images with fewer colors exhibit quadratic shift and bias from L1 and L3 but adhere to L2. Randomized Hilbert fractal FCIs better match the laws than basic-to-AI-based simulations. Those results are of interest in Neural Networks, out-of-equilibrium physics, and spectral imagery.

First report of betalain production from endolichenic Bacillus sp. LDAB-1 from Dirinaria aegilita: Insights from novel quantification methodology of image processing.

Pigments are widely used in food supplements envisaging attractive colors along with health benefits. The desired advancements in the nutraceutical and antioxidant properties of pigments utilized in food products necessitate the search for novel additives. The present study is the first in the field to report the pigment-producing endolichenic bacteria, Bacillus sp. LDAB-1 from Dirinaria aegilita. Morphological, biochemical, and molecular characterization of the bacterium emphasizes that ideal pigment production occurs when utilizing sucrose and sodium nitrate. The pigment was salted out and dialyzed for further qualitative characterization using ultraviolet-visible, fluorescence, and Fourier transforminfraredspectra and the results corroborated the presence of betalains. The antioxidant activity of betalain is closer to the efficiency of α-tocopherol, which confers the pigment properties for antioxidant and nutraceutical significance. An optimal methodology for pigment affirmation is an issue when using an alternative methodology. Hence, the present assessment employs a comparative analysis of findings from both a spectrophotometric method and image processing technology encompassing RGB, CMYK, YCbCr, and L*a*b*color space models. Amongst these, the L*a*b* model potentially provides an effective modality for determining the pigment concentration. Bland-Altman plot analysis indicates similar consistency levels in betalain quantification by both methods at 95% confidence intervals, affirming the integrity and consistency of color image processing technology. Consequently, the present study represents novelty and innovativeness in reporting endolichenic Bacillus sp. LDAB-1 from D. aegilita and a rational image optimization protocol for pigment elucidation characteristics.

Modified snake optimizer based multi-level thresholding for color image segmentation of agricultural diseases

Modified snake optimizer based multi-level thresholding for color image segmentation of agricultural diseases

PHNNs: Lightweight Neural Networks via Parameterized Hypercomplex Convolutions.

Hypercomplex neural networks have proven to reduce the overall number of parameters while ensuring valuable performance by leveraging the properties of Clifford algebras. Recently, hypercomplex linear layers have been further improved by involving efficient parameterized Kronecker products. In this article, we define the parameterization of hypercomplex convolutional layers and introduce the family of parameterized hypercomplex neural networks (PHNNs) that are lightweight and efficient large-scale models. Our method grasps the convolution rules and the filter organization directly from data without requiring a rigidly predefined domain structure to follow. PHNNs are flexible to operate in any user-defined or tuned domain, from 1-D to [Formula: see text] regardless of whether the algebra rules are preset. Such a malleability allows processing multidimensional inputs in their natural domain without annexing further dimensions, as done, instead, in quaternion neural networks (QNNs) for 3-D inputs like color images. As a result, the proposed family of PHNNs operates with 1/n free parameters as regards its analog in the real domain. We demonstrate the versatility of this approach to multiple domains of application by performing experiments on various image datasets and audio datasets in which our method outperforms real and quaternion-valued counterparts. Full code is available at: https://github.com/eleGAN23/HyperNets.

Improved vigenere using affine functions surrounded by two genetic crossovers for image encryption

This paper presents an improved method for encrypting color images, surpassing the effectiveness of genetic crossover and substitution operations. The technique incorporates dynamic random functions to enhance the integrity of the resulting vector, increasing temporal complexity to thwart potential attacks. The improvement involves integrating genetic crossover and utilizing two extensive pseudorandom replacement tables derived from established chaotic maps in cryptography. Following the controlled vectorization of the original image, our approach initiates with a first genetic crossover inspired by deoxyribonucleic acid (DNA) behavior at the pixel level. This genetic crossover is succeeded by a confusion-diffusion lap, reinforcing the connection between encrypted pixels and their neighboring counterparts. The confusion-diffusion process employs dynamic pseudorandom affine functions at the pixel level. Then a second genetic crossover operator is applied. Simulations conducted on a diverse set of images with varying sizes and formats showcase the robustness of our method against statistical, brute-force, and differential attacks.

Scrambled Encryption Approach for Color Images Based on 9-D Chaotic Systems with 3-D Substitution Bit Levels

Scrambled Encryption Approach for Color Images Based on 9-D Chaotic Systems with 3-D Substitution Bit Levels

Optimization of CPBIS methods applied on enhanced fibrin microbeads approach for image segmentation in dynamic databases

In the empire of image processing and computer vision, the demand for advanced segmentation techniques has intensified with the growing complexity of visual data. This study focuses on the innovative paradigm of fuzzy mountain-based image segmentation, a method that harnesses the power of fuzzy logic and topographical inspiration to achieve nuanced and adaptable delineation of image regions. This research primarily concentrates on determining the age of tigers, a critical and challenging task in the current scenario. The primary objectives include the development of a comprehensive framework for FMBIS and an in-depth investigation into its adaptability to different image characteristics. This research work incorporates those domains of image processing and data mining to predict the age of the tiger using different kinds of color images. Fuzzy mountain-based pixel segmentation arises from the need to capture the subtle gradients and uncertainties present in images, offering a novel approach to achieving high-fidelity segmentations in diverse and complex scenarios. The proposed methods enable image enhancement and filtering and are then assessed during process time, retrieval time, to give a more accurate and reduced error rate for producing higher results for real-time tiger image database.

Deep-learning-assisted inverse design of polarization-multiplexed structural color filters with ultrahigh saturation based on all-dielectric metasurface

Although great progress has been made for metasurface-based structural color filters, there are still significant challenges in encoding metasurfaces with complex color images, especially in high saturation or multi-channel cases, where traditional iterative optimization calculations demand substantial computational resources. Here, we proposed a dual-channel multilayered all-dielectric metasurface that can generate structural colors accounting for 224% Standard RGB space, 168% Adobe RGB space and 78% of the 1931 CIE chromaticity space under vertically-polarized incidence. A deep learning-based designing approach is introduced to significantly reduce the computing consumption while maintaining high accuracy. Numerical simulations further demonstrated that the proposed design method is effective for encoding both complementary and independent images with high saturation and wide color gamut. Therefore, the proposed color filters may find potential applications in image display, information encryption and optical anti-counterfeiting.

Improved Vigenere approach incorporating pseudorandom affine functions for encrypting color images

This article presents an improvement to the traditional Vigenere encryption method, specifically adapted for the encryption of color images. This enhancement relies on the use of two chaotic maps widely employed in the field of cryptography. After vectorizing the original image and calculating the initialization value, which alters the seeding pixel to trigger the encryption process, our approach integrates two new large substitution tables. These tables are linked to confusion and diffusion functions, incorporating multiple reversible pseudo-random affine functions at the pixel level. Finally, a global permutation is applied to the entire resulting vector to increase the temporal complexity of potential attacks on our system. Simulations conducted on a diverse set of images of various sizes and formats demonstrate the resilience of our approach against any unexpected attacks.

Defect detection of printed circuit board based on adaptive key-points localization network

Many deep neural networks (DNNs) have been applied in the defect detection of products. Due to the irregular and small defects on printed circuit boards (PCB), it is difficult for the DNN-based defect detection models to achieve good detection performance. In this paper, a new DNN, adaptive key point localization network (AKPLNet) is proposed for PCB defect detection. Firstly, residual pyramid heat mapping network (RFHNet) that is composed of ResNet50_FPN and thermodynamic mechanism (TM), is used to perform multi-scale feature extraction and defect location. Secondly, an adaptive tree structure region proposal network (AT-RPN) based on tree structure Parzen estimation is proposed to obtain the predicted regions of the target, which reduces the need for large number of priori knowledge during the detection process. Finally, a key point regression algorithm is proposed to locate defects accurately. The defect detection performance of AKPLNet is validated on two PCB datasets. The mean average precision (mAP) of AKPLNet reaches 96.9% and 99.0% on PCB-Master dataset with the color images and DeepPCB-Master dataset with the grayscale images, improving 2.1% and 2.3% compared with Yolov7, respectively. The testing results demonstrate that AKPLNet achieves the better detection accuracy than those state-of-the-art methods.

Hyperbolic tangent penalty function for edge-preserving image filtering

Edge-preserving image filtering provides a common interface for a variety of edge-aware image processing tasks. It is essential to the field of image processing and computational photography. Recent attempts at optimization models with non-convex regularization have shown promising performance in edge-preserving image filtering. In this paper, we propose a novel non-convex regularization term based on a hyperbolic tangent penalty function, which is shown to be more edge-preserving than popular non-convex penalty functions. We embed the proposed regularization term in an optimization model for edge-preserving image filtering. To solve the model with the non-convex regularization, we propose an efficient solution based on the additive half quadratic minimization and Fourier domain optimization. We have conducted extensive experiments to evaluate the proposed filter. Both quantitative and qualitative results demonstrate that our filter benefits various applications. Furthermore, the proposed filter is highly efficient and renders real-time processing of 720P color images on a modern GPU.

A Fast Browsing System with Colour Image Enhancement for Surveillance Videos

Abstrac: Surveillance cameras have been widely installed in large cities to monitor and record human activities for different applications. Since surveillance cameras often record all events 24 hours/day, it necessarily takes huge workforce watching surveillance videos to search for specific targets,thus a system that helps the user quickly look for targets of interest is highly demanded. This paper proposes a quick surveillance video browsing system with colour image enhancement. The basic idea is to collect all of moving objects which carry the most significant information in surveillance videos to construct a corresponding compact video. The compact video rearranges the spatiotemporal coordinates of moving objects to enhance the compression, but the temporal relationships among moving objects are still kept. Thecompact video can preserve the essential activities involved in the original surveillance video. This paper presents the details of browsing system and the approach to producing the compact video from a source surveillance video. At the end we will get the compact video with high resolution.

Global robust exponential synchronization of neutral-type interval Cohen–Grossberg neural networks with mixed time delays

Global robust exponential synchronization of neutral-type interval Cohen–Grossberg neural networks (NTICGNNs) with mixed time delays is analyzed in this paper. The system model, in which unrepresentable using vector-matrices, renders certain methods unsuitable for addressing issues within the vector-matrix framework. To address this limitation, a direct method based on system solutions is proposed, and sufficient conditions are provided to achieve global robust exponential synchronization of the considered NTICGNNs. This approach results in a global robust exponential synchronization criterion without the need to establish any Lyapunov-Krasovskii functionals. The applicability of the obtained synchronization conditions is validated using two numerical examples. Additionally, the obtained theoretical results have been applied to solve a quadratic programming problem and to encrypt and decrypt color images, demonstrating the practical application value of our research results. Notably, this study presents the first global robust exponential synchronization analysis for the considered NTICGNNs and proposes a novel approach grounded in system solutions.

Comparison of Signal to Noise Ratio of Colored and Gray Scale Image in Clustered Condition from the Contours of the Images with the Help of Different Image Filtering Method

As we know the image can be processed with the help of different types of coding for example mat-lab. Here in this paper we are primarily focusing on some common filtering methodologies [5]related to image contour in clustered conditions. For filtering purpose in this paper we have used three different filtering technologies such as prewitt [3], sobel [3], canny [3] filtering. But on the other hand we have used both colored [1] and non-colored [3] images for clustering operations. Our main aim in this paper to show variations of signal to noise ratios for the colored and non-colored contour images with and without filtering. As per my request study the discussion of results very carefully to realize the deeper meaning of the journal [4].

Multiple Lagrangian Jet‐Core Structures in the Malvinas Current

AbstractThe Malvinas Current (MC) in the Southwestern Atlantic is characterized as a system of multiple Lagrangian jets. This is done by applying geodesic theory of Lagrangian Coherent Structures (LCSs) on altimetry‐derived velocity. The number and spatial disposition of jet‐cores organizing the MC are found to vary in time influenced by topographic waves propagating along the current. The Lagrangian analysis reveals that permanent jet‐cores tend to approach one another upstream, yet do not merge into a single jet as the Eulerian analysis of the data suggests. Independent support for the existence of multiple jets is provided by trajectories of surface drifters from a deployment of unparalleled dimensions for the region and ocean color images. The drifters develop the characteristic boomerang‐shaped patterns into which passive tracers straddling jet‐cores deform and exhibit Lagrangian metrics that are largely consistent with those derived from altimetry data. Ocean color imagery reveals similar “chevrons” and suggest upwelling along the jets, which is consistent with a new solution of arrested topographic wave dynamics. This observation and the finding that the MC jet system is influenced by propagating waves suggests that the MC variability may have major implications in the modulation of primary productivity on synoptic timescales. Drifters flowing into the open ocean, exhibit organizing patterns that are largely shaped by mesoscale circulation and essentially differ from those observed along the MC. Evidence of this observation is provided on the basis of LCS analysis and the calculation of a quasi‐objective Lagrangian metric derived directly from drifter trajectories.